English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, there has been a growing demand and interest in developing methods for analyzing smartphone logs to extract traffic safety information. Because the log is high time resolution and closely related to user activities but fragmentary and myopic, it is difficult for currently available collision probability based quantitative risk assessment methods to create traffic safety maps automatically from the driving log which require all of concrete information about a collision for example, size of vehicle, speed of pedestrian. This paper proposes a computable risk measurement method for building traffic safety maps with the logs of different users' driving, which does not discuss collision probability. The proposal is designed to compute differences in the recognition of the road environment among road users mathematically. Drivers differ in their recognition, judgment, and handling of a given situation. Suppose that a difference in recognition among users in the same situation is a signal of danger. This signal is easy to calculate by Poisson process. Each user's recognition of road environment and the safety map integrated from the collection of the recognition are generated fully automated. A real-world experiment was carried out, and the results show that the assumption and the proposed method succeeded in generating an accurate and effective traffic safety map.

Building Safety Road Maps Based on Difference of Judgment of Road Users by their Smartphone